{
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   "source": [
    "# Integer Linear Programming"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "877ae3cf-10cc-4e51-a428-eb57687d8d18",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "In Integer Linear Programming (ILP), we seek to find a vector of integer numbers that maximizes (or minimizes) a linear cost function under a set of linear equality or inequality constraints [[1]](#ILP). In other words, it is an optimization problem where the cost function to be optimized and all the constraints are linear and the decision variables are integer.\n",
    "\n",
    "\n",
    "\n",
    "## Mathematical Formulation\n",
    "The ILP problem can be formulated as follows:\n",
    "\n",
    "Given an $n$-dimensional vector $\\vec{c} = (c_1, c_2, \\ldots, c_n)$, an $m \\times n$ matrix $A = (a_{ij})$ with $i=1,\\ldots,m$ and $j=1,\\ldots,n$, and an $m$-dimensional vector $\\vec{b} = (b_1, b_2, \\ldots, b_m)$, find an $n$-dimensional vector $\\vec{x} = (x_1, x_2, \\ldots, x_n)$ with integer entries that maximizes (or minimizes) the cost function:\n",
    "\n",
    "\\begin{align*}\n",
    "\\vec{c} \\cdot \\vec{x} = c_1x_1 + c_2x_2 + \\ldots + c_nx_n\n",
    "\\end{align*}\n",
    "\n",
    "subject to the constraints:\n",
    "\n",
    "\\begin{align*}\n",
    "A \\vec{x} & \\leq \\vec{b} \\\\\n",
    "x_j & \\geq 0, \\quad j = 1, 2, \\ldots, n \\\\\n",
    "x_j & \\in \\mathbb{Z}, \\quad j = 1, 2, \\ldots, n\n",
    "\\end{align*}\n",
    "\n",
    "\n",
    "\n",
    "# Solving with the Classiq platform\n",
    "\n",
    "We go through the steps of solving the problem with the Classiq platform, using QAOA algorithm [[2](#QAOA)]. The solution is based on defining a pyomo model for the optimization problem we would like to solve."
   ]
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   "source": [
    "from typing import cast\n",
    "\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import pyomo.core as pyo\n",
    "from IPython.display import Markdown, display\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8517ef73-faf6-4fd8-9db8-4088551398e0",
   "metadata": {},
   "source": [
    "## Building the Pyomo model from a graph input\n",
    "\n",
    "We proceed by defining the pyomo model that will be used on the Classiq platform, using the mathematical formulation defined above:"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "import numpy as np\n",
    "import pyomo.core as pyo\n",
    "\n",
    "\n",
    "def ilp(a: np.ndarray, b: np.ndarray, c: np.ndarray, bound: int) -> pyo.ConcreteModel:\n",
    "    # model constraint: a*x <= b\n",
    "    model = pyo.ConcreteModel()\n",
    "    assert b.ndim == c.ndim == 1\n",
    "\n",
    "    num_vars = len(c)\n",
    "    num_constraints = len(b)\n",
    "\n",
    "    assert a.shape == (num_constraints, num_vars)\n",
    "\n",
    "    model.x = pyo.Var(\n",
    "        # here we bound x to be from 0 to to a given bound\n",
    "        range(num_vars),\n",
    "        domain=pyo.NonNegativeIntegers,\n",
    "        bounds=(0, bound),\n",
    "    )\n",
    "\n",
    "    @model.Constraint(range(num_constraints))\n",
    "    def monotone_rule(model, idx):\n",
    "        return a[idx, :] @ list(model.x.values()) <= float(b[idx])\n",
    "\n",
    "    # model objective: max(c * x)\n",
    "    model.cost = pyo.Objective(expr=c @ list(model.x.values()), sense=pyo.maximize)\n",
    "\n",
    "    return model"
   ]
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   "cell_type": "code",
   "execution_count": 3,
   "id": "6e5295f4-7ba6-4ff6-8782-1c4c2c7f85e4",
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   "source": [
    "A = np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]])\n",
    "b = np.array([1, 2, 3])\n",
    "c = np.array([1, 2, 3])\n",
    "\n",
    "# Instantiate the model\n",
    "ilp_model = ilp(A, b, c, 4)"
   ]
  },
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     "text": [
      "2 Set Declarations\n",
      "    monotone_rule_index : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    3 : {0, 1, 2}\n",
      "    x_index : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    3 : {0, 1, 2}\n",
      "\n",
      "1 Var Declarations\n",
      "    x : Size=3, Index=x_index\n",
      "        Key : Lower : Value : Upper : Fixed : Stale : Domain\n",
      "          0 :     0 :  None :     4 : False :  True : NonNegativeIntegers\n",
      "          1 :     0 :  None :     4 : False :  True : NonNegativeIntegers\n",
      "          2 :     0 :  None :     4 : False :  True : NonNegativeIntegers\n",
      "\n",
      "1 Objective Declarations\n",
      "    cost : Size=1, Index=None, Active=True\n",
      "        Key  : Active : Sense    : Expression\n",
      "        None :   True : maximize : x[0] + 2*x[1] + 3*x[2]\n",
      "\n",
      "1 Constraint Declarations\n",
      "    monotone_rule : Size=3, Index=monotone_rule_index, Active=True\n",
      "        Key : Lower : Body                     : Upper : Active\n",
      "          0 :  -Inf :       x[0] + x[1] + x[2] :   1.0 :   True\n",
      "          1 :  -Inf : 2*x[0] + 2*x[1] + 2*x[2] :   2.0 :   True\n",
      "          2 :  -Inf : 3*x[0] + 3*x[1] + 3*x[2] :   3.0 :   True\n",
      "\n",
      "5 Declarations: x_index x monotone_rule_index monotone_rule cost\n"
     ]
    }
   ],
   "source": [
    "ilp_model.pprint()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17ea14ec-dbb7-487c-b4f1-cabc8d5e3c29",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Setting Up the Classiq Problem Instance\n",
    "\n",
    "In order to solve the Pyomo model defined above, we use the Classiq combinatorial optimization engine. For the quantum part of the QAOA algorithm (`QAOAConfig`) - define the number of repetitions (`num_layers`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "816b468f-a59f-4f2f-8337-4a9d66548425",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2024-05-07T16:03:06.767070Z"
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   "source": [
    "from classiq import construct_combinatorial_optimization_model\n",
    "from classiq.applications.combinatorial_optimization import OptimizerConfig, QAOAConfig\n",
    "\n",
    "qaoa_config = QAOAConfig(num_layers=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db34d5ac-6877-4285-8dec-7bf7b37eb783",
   "metadata": {},
   "source": [
    "For the classical optimization part of the QAOA algorithm we define the maximum number of classical iterations (`max_iteration`) and the $\\alpha$-parameter (`alpha_cvar`) for running CVaR-QAOA, an improved variation of the QAOA algorithm [[3](#cvar)]:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e41d0dd3-4135-4330-9ba3-c1b30c339a74",
   "metadata": {
    "execution": {
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    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
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   "outputs": [],
   "source": [
    "optimizer_config = OptimizerConfig(max_iteration=90, alpha_cvar=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "214d6051-43b8-4b9d-8454-f9cdb62b4cf0",
   "metadata": {},
   "source": [
    "Lastly, we load the model, based on the problem and algorithm parameters, which we can use to solve the problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0243019c-6fc3-435f-b6ec-8b4355d6660c",
   "metadata": {
    "execution": {
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    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "qmod = construct_combinatorial_optimization_model(\n",
    "    pyo_model=ilp_model,\n",
    "    qaoa_config=qaoa_config,\n",
    "    optimizer_config=optimizer_config,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fcc3812-c9d0-421c-84bb-38047297b33f",
   "metadata": {},
   "source": [
    "We also set the quantum backend we want to execute on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "53bc041f-065c-44d2-b220-dafd9d0504ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:03:10.003163Z",
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    },
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   "source": [
    "from classiq import set_execution_preferences\n",
    "from classiq.execution import ClassiqBackendPreferences, ExecutionPreferences\n",
    "\n",
    "backend_preferences = ExecutionPreferences(\n",
    "    backend_preferences=ClassiqBackendPreferences(backend_name=\"aer_simulator\")\n",
    ")\n",
    "\n",
    "qmod = set_execution_preferences(qmod, backend_preferences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "62ec28b3-cb49-411a-8c4a-8004fff6c105",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:03:10.050654Z",
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    }
   },
   "outputs": [],
   "source": [
    "from classiq import write_qmod\n",
    "\n",
    "write_qmod(qmod, \"integer_linear_programming\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "943291f0-6a9f-4286-a69d-ef13a0a12ef6",
   "metadata": {},
   "source": [
    "## Synthesizing the QAOA Circuit and Solving the Problem\n",
    "\n",
    "We can now synthesize and view the QAOA circuit (ansatz) used to solve the optimization problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1d71e29a-5d53-49c4-84b2-45f59be4da31",
   "metadata": {
    "execution": {
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    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening: https://platform.classiq.io/circuit/183bcd6c-83c8-4610-828f-d7cd40f9fb85?version=0.41.0.dev39%2B79c8fd0855\n"
     ]
    }
   ],
   "source": [
    "from classiq import show, synthesize\n",
    "\n",
    "qprog = synthesize(qmod)\n",
    "show(qprog)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80238cf9-d7bd-46e5-9d48-b7cf23a6b304",
   "metadata": {},
   "source": [
    "We now solve the problem by calling the `execute` function on the quantum program we have generated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "62d12d20-1c80-4a9e-bb6b-b1fddc6cbe40",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:03:15.848412Z",
     "iopub.status.busy": "2024-05-07T16:03:15.848000Z",
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     "shell.execute_reply": "2024-05-07T16:03:33.644679Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from classiq import execute\n",
    "\n",
    "res = execute(qprog).result()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "620ea6a0-cd05-41a9-a2ed-9631c680d2e6",
   "metadata": {},
   "source": [
    "We can check the convergence of the run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "02454398-b229-403c-824a-b1eb539fbc1f",
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   "outputs": [
    {
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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from classiq.execution import VQESolverResult\n",
    "\n",
    "vqe_result = res[0].value\n",
    "vqe_result.convergence_graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "615ed612-b835-4bf0-aa92-92d30ef8006d",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Optimization Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "670eddd3-2da7-4a88-b571-7884ef24f60c",
   "metadata": {},
   "source": [
    "We can also examine the statistics of the algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "516d78ba-2951-46eb-b1af-efe877513556",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:03:33.675397Z",
     "iopub.status.busy": "2024-05-07T16:03:33.674963Z",
     "iopub.status.idle": "2024-05-07T16:03:37.107224Z",
     "shell.execute_reply": "2024-05-07T16:03:37.106517Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probability</th>\n",
       "      <th>cost</th>\n",
       "      <th>solution</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.019</td>\n",
       "      <td>3.0</td>\n",
       "      <td>[0, 0, 1]</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.015</td>\n",
       "      <td>3.0</td>\n",
       "      <td>[0, 0, 1]</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.011</td>\n",
       "      <td>2.0</td>\n",
       "      <td>[0, 1, 0]</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>0.002</td>\n",
       "      <td>2.0</td>\n",
       "      <td>[0, 0, 2]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.025</td>\n",
       "      <td>2.0</td>\n",
       "      <td>[0, 1, 0]</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     probability  cost   solution  count\n",
       "6          0.019   3.0  [0, 0, 1]     19\n",
       "15         0.015   3.0  [0, 0, 1]     15\n",
       "24         0.011   2.0  [0, 1, 0]     11\n",
       "106        0.002   2.0  [0, 0, 2]      2\n",
       "3          0.025   2.0  [0, 1, 0]     25"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from classiq.applications.combinatorial_optimization import (\n",
    "    get_optimization_solution_from_pyo,\n",
    ")\n",
    "\n",
    "solution = get_optimization_solution_from_pyo(\n",
    "    ilp_model, vqe_result=vqe_result, penalty_energy=qaoa_config.penalty_energy\n",
    ")\n",
    "\n",
    "optimization_result = pd.DataFrame.from_records(solution)\n",
    "optimization_result.sort_values(by=\"cost\", ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "687f492b-a4a5-49c6-964c-8959b035bb93",
   "metadata": {},
   "source": [
    "And the histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "31a4e74d-b2b8-42e0-826d-de7b51de1fe8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:03:37.110324Z",
     "iopub.status.busy": "2024-05-07T16:03:37.109906Z",
     "iopub.status.idle": "2024-05-07T16:03:37.306818Z",
     "shell.execute_reply": "2024-05-07T16:03:37.306110Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'cost'}>]], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "optimization_result.hist(\"cost\", weights=optimization_result[\"probability\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3a890a1-c5d4-409d-b9a3-d7ffd4fdd6c0",
   "metadata": {},
   "source": [
    "Let us plot the solution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4326e84b-26f6-4ea9-a53b-090fb3658b8c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:03:37.309483Z",
     "iopub.status.busy": "2024-05-07T16:03:37.308945Z",
     "iopub.status.idle": "2024-05-07T16:03:37.313874Z",
     "shell.execute_reply": "2024-05-07T16:03:37.313442Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 1]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_solution = optimization_result.solution[optimization_result.cost.idxmax()]\n",
    "best_solution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "149932e1-bfa8-4c27-b5f9-037e74eba400",
   "metadata": {},
   "source": [
    "## Comparison to a classical solver"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dde1905d-aeff-4297-a9d3-ad14910e6161",
   "metadata": {},
   "source": [
    "Lastly, we can compare to the classical solution of the problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5a7ca4b6-25a0-46dd-b5cc-de6a639a6f57",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:03:37.316362Z",
     "iopub.status.busy": "2024-05-07T16:03:37.315830Z",
     "iopub.status.idle": "2024-05-07T16:03:37.375955Z",
     "shell.execute_reply": "2024-05-07T16:03:37.375336Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classical solution: [0, 0, 1]\n"
     ]
    }
   ],
   "source": [
    "from pyomo.opt import SolverFactory\n",
    "\n",
    "solver = SolverFactory(\"couenne\")\n",
    "solver.solve(ilp_model)\n",
    "classical_solution = [int(pyo.value(ilp_model.x[i])) for i in range(len(ilp_model.x))]\n",
    "print(\"Classical solution:\", classical_solution)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e82b5953-122a-4707-8ab6-f741f14f13a5",
   "metadata": {
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     "name": "#%% md\n"
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   },
   "source": [
    "\n",
    "## References\n",
    "\n",
    "<a id='MVC'>[1]</a>: [Integer Programming (Wikipedia).](https://en.wikipedia.org/wiki/Integer_programming)\n",
    "\n",
    "<a id='QAOA'>[2]</a>: [Farhi, Edward, Jeffrey Goldstone, and Sam Gutmann. \"A quantum approximate optimization algorithm.\" arXiv preprint arXiv:1411.4028 (2014).](https://arxiv.org/abs/1411.4028)\n",
    "\n",
    "<a id='cvar'>[3]</a>: [Barkoutsos, Panagiotis Kl, et al. \"Improving variational quantum optimization using CVaR.\" Quantum 4 (2020): 256.](https://arxiv.org/abs/1907.04769)\n"
   ]
  }
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